Professional certification in Programming

APACHE SPARK AND SCALA TRAINING

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Duration & Study Mode:
Full time, Part-time, Evening and Weekends, Virtual online
Location:
London, Flexible online
Duration: 5 Days / 5 Weeks

Apache Spark is an open-source, high-speed cluster computing solution for analysing enormous amounts of data. Spark is the most comprehensive tool, and many significant corporations have adopted it throughout the world. The Apache Spark and Scala Certification give attendees in-depth information and practical abilities to help them improve their Big Data Spark expertise. During this workshop, participants will learn about Spark and its ecosystem, Spark Streaming, Spark SQL, RDD, and Scala. This course will provide in-depth knowledge of the Scala programming language, YARN, HDFS, Sqoop, Flume, Spark GraphX, and Kafka messaging systems. Participants will receive a certificate if they pass the exam after finishing this session.

Prerequisites

  • There are no prerequisites for this course.
  • Participants are recommended to have a fundamental understanding of Java, databases, query languages, and SQL.

What will you gain after this course

  • Use Scala and Spark applications to work on projects.
  • Learn about Scala classes and how to use them to match patterns.
  • Learn how to use Python, Java, and Scala to create Spark applications.

Jobs you can get
with a Apache Spark and Scala

  • Spark Scala Developer
  • Data Platform Engineer
  • Apache Spark Engineer
  • Big Data Engineer
  • Senior Business Analyst

Corporate Group Training

  • Customized Training
  • Onsite / Virtual
  • Instructor-led Delivery
  • For small to large groups

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Who is this certification for?

  • This course is for students interested in pursuing a career in Big Data.
  •  This course is also intended for: 
  • Senior IT Professionals
  • DWProfessionals
  • Data Scientists and Analytics Professionals
  • Developers and Architects
  • Testing Professionals
  • Software Architects
  • BI and ETL Professionals
  • Engineers and Developers


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Small Groups

With small groups of students, our instructors can work closely with each student.

Schedule
Flexible Class Schedules

Our class schedules are flexible on weekdays, weekend, or evenings to suit your schedule.

Instructors
Experienced Instructors

Our instructors follow a modified are personalized approach to engage students during class

Lab-Facilities
Hi-Tech Lab Facilities

Our students can access our lab facilities anytime for practical experience during and after studies.

Syllabus

  • Introduction to Scala and Deployment of Scala for Big Data applications
  • An Overview of Apache Spark analytics
  • Importance of Scala
  • The Concept of REPL (Read Evaluate Print Loop)
  • Deep Dive into Scala Pattern Matching
  • Type Interface and Higher-Order Function
  • Currying and Traits
  • Application Space
  • Scala for Data Analysis
  • Introduction to Scala Interpreter
  • Static Object Timer in Scala
  • Implicit Classes in Scala and Testing String Equality in Scala
  • Understand the Concept of Currying in Scala
  • Different Classes in Scala
  • Introduction to Classes concept
  • Understanding the Constructor Overloading
  • Different Abstract Classes
  • The Hierarchy Types in Scala
  • The Concept of Object Equality and Val and Var Methods in Scala
  • Introduction to Sealed Traits
  • Wild and Constructor
  • Tuple
  • Variable and Constant pattern
  • Introduction to Traits in Scala
  • The Advantages of Traits
  • Linearisation of Traits and The Java Equivalent
  • Avoiding of Boilerplate Code
  • Implementation of Traits in Scala and Java
  • Handling of Multiple Traits Extending
  • Introduction to Scala Collections
  • Classification of Collections
  • The Difference Between Iterator and Iterable in Scala
  • Example of List Sequence in Scala
  • The Types of Collections in Scala
  • Mutable and Immutable Collections
  • Lists and Arrays in Scala
  • The List Buffer and Array Buffer
  • Queue in Scala
  • Double-Ended Queue Deque
  • Stacks and Sets
  • Maps and Tuples in Scala
  • What is Scala Packages and Imports
  • The Selective Imports and Test Classes
  • Introduction to JUnit test Class
  • JUnit Interface via JUnit 3 suite for Scala Test
  • Packaging of Scala Applications in Directory Structure
  • Example of Spark Split and Spark Scala
  • What are Spark and Spark Stack
  • How Spark Overcomes the Drawbacks of working Map Reduce
  • Introduction to in-memory Map Reduce
  • Interactive Operations on Map Reduce
  • Fine vs Coarse-Grained Update
  • Spark Hadoop YARN
  • HDFS and YARN Revision
  • How it is Better Hadoop
  • Deploying Spark without Hadoop
  • Spark History Server
  • Cloudera Distribution
  • Spark Installation Guide and Configuration
  • Memory Management
  • Executor Memory vs Driver Memory
  • Working with Spark Shell
  • Concept of Resilient Distributed Datasets (RDD)
  • Learning to do Functional Programming in Spark
  • The Architecture of Spark
  • Spark RDD and Creating RDDs
  • RDD Partitioning
  • Operations and Transformation in RDD
  • Deep Dive into Spark RDDs
  • The RDD General Operations
  • A Read-Only Partitioned Collection of Records
  • Using the Concept of RDD for Faster and Efficient Data Processing
  • RDD Action for Collect
  • Count and Collects Map
  • Saveastextfiles
  • Pair RDD Functions
  • Introduction to Key-Value Pair in RDDs
  • How Spark makes Map-Reduce Operations Faster
  • Different Operations of RDD
  • Map Reduce Interactive Operations
  • Fine and Coarse-Grained Update
  • Comparing the Spark Applications with Spark Shell
  • Creating a Spark Application using Scala or Java
  • Deploying a Spark Application
  • Scala Built Application and Creation of Mutable List
  • Set and Set Operations
  • List and Tuple
  • Concatenating List
  • Creating an Application using SBT
  • Deploying Application using Maven
  • The Web User Interface of Spark Application
  • A Real-World Example of Spark
  • Configuring of Spark
  • Spark Parallel Processing
  • Deploying on a Cluster
  • Introduction to Spark partitions
  • File-Based Partitioning of RDDs
  • What is HDFS
  • Data Locality
  • Mastering the Technique of Parallel Operations
  • Comparing Repartition & Coalesce
  • RDD Actions
  • The Execution Flow in Spark
  • RDD Persistence Overview
  • Spark Execution Flow
  • Spark Terminology
  • Distribution shared Memory vs RDD
  • RDD Limitations and RDD Lineage
  • Spark Shell Arguments and Distributed Persistence
  • Key/Value Pair for Sorting Implicit Conversion like CountByKey
  • ReduceByKey and SortByKey and AggregataeByKey
  • Spark Streaming Architecture
  • Writing Streaming Program Coding
  • Processing of Spark Stream and Processing Spark Discretised Stream (DStream)
  • The Context of Spark Streaming
  • Streaming Transformation and Flume Spark Streaming
  • Request Count and Dstream
  • Multi Batch Operation and Sliding Window Operations
  • Advanced-Data Sources and Different Algorithms
  • The Concept of the Iterative Algorithm in Spark
  • Analysing with Spark Graph Processing
  • Introduction to K-Means and Machine Learning
  • Various Variables in Spark like Shared Variables
  • Broadcast Variables and Accumulators
  • Describe Spark SQL
  • The Context of SQL in Spark
  • Working with XML Data
  • Parquet Files
  • JSON support in Spark SQL
  • Creating a Hive Context
  • Writing Data Frame to Hive
  • Reading JDBC files
  • Introduction to Data Frames in Spark
  • Creating Data Frames
  • Manual Inferring of Schema
  • Working with CSV Files
  • Reading JDBC Tables
  • Data Frame to JDBC
  • User-Defined Functions in Spark SQL
  • Shared Variable and Accumulators
  • Understanding to Query and Transform Data in Data Frames
  • Introduction to various variables in Spark like Shared Variables
  • Broadcast Variables
  • Learning About Accumulators
  • The Common Performance Issues
  • Troubleshooting the Performance Problems
  • Learning about the Scheduling and Partitioning in Spark
  • Hash Partition and Range Partition
  • Scheduling within and Around Applications
  • Static Partitioning and Dynamic Sharing
  • Fair Scheduling and High Order Functions
  • Map Partition with index
  • The Zip and GroupByKey
  • Spark Master High Availability
  • Standby Masters with Zookeeper
  • Single Node Recovery with Local File System

When would you like to start?

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01/05/202210:00 – 17:00Sun Only5 WeeksClassroom / OnlinePlaces available Future Dates/Request Price
07/05/202210:00 – 17:00Sat Only5 WeeksClassroom / OnlinePlaces available Future Dates/Request Price
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10/07/202210:00 – 16:00 Sun Only5 WeeksClassroom / OnlinePlaces available Future Dates/Request Price
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03/01/202318:30 – 21:30Tue / Thu5 WeeksClassroom / OnlinePlaces available Future Dates/Request Price

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